Direct Methods.
Direct methods [1]}, [2]}, [3]}, [4]} estimate 3D detections directly from images without predicting an intermediate 3D scene representation. Rather, direct methods [5]}, [6]}, [7]}, [8]} can incorporate the geometric relationship between the 2D image plane and 3D space to assist with detections. For example, object keypoints can be estimated on the image plane, in order to assist in 3D box construction using known geometry [9]}, [10]}. M3D-RPN [8]} introduces depth-aware convolutions that divides the input row-wise and learns non-shared kernels for each region, to learn location specific features that correlate to regions in 3D space. Shape estimation can be performed for objects in the scene to create an understanding of 3D object geometry. Shape estimates can be supervised from labeled vertices of 3D CAD models [12]}, [13]}, from LiDAR scans [14]}, or directly from input data in a self-supervised manner [15]}. A drawback for direct methods is that detections are generated directly from 2D images, without access to explicit depth information, usually resulting in reduced performance in localization relative to other methods.
